setPackageSeedTP: Set the initial package seed
In TPmsm: Estimation of Transition Probabilities in Multistate Models

setPackageSeedTP

R Documentation

Set the initial package seed

Description

The random number generator (RNG) with multiple independent streams developed by L'Ecuyer et al. (2002) is used for parallel computation of uniform pseudorandom numbers. Package TPmsm makes extensive use of uniform pseudorandom numbers, particularly for the bootstrapping statistical techniques and for the generation of univariate and multivariate pseudorandom data. This function defines the seed for the creation of RNG streams.

Usage

setPackageSeedTP(seed=12345)

Arguments

seed

A vector of one to six integers. Defaults to rep(x=12345, times=6).

Details

If the user defines a vector with length lower than six as seed, then the seed is internally defined as a vector of length six with the first elements equal to the user defined values, and the leaving elements equal to 12345. If a vector with more than six elements is provided as seed, then only the first six elements are used.

Value

Invisibly returns NULL.

Note

When package TPmsm loads, an initial set of RNG streams is created, one stream for each thread available for parallel computation. The initial set of RNG streams is created from the package seed c(12345, 12345, 12345, 12345, 12345, 12345). Every time this function is called, the old set of RNG streams is deleted, and a new set of RNG streams is created from the user defined package seed. After the creation of each new RNG stream, the internally stored package seed changes. So each RNG stream is created from a different package seed, and yields different sets of pseudorandom numbers.

Author(s)

Artur Araújo, Javier Roca-Pardiñas and Luís Meira-Machado

References

Araújo A, Meira-Machado L, Roca-Pardiñas J (2014). TPmsm: Estimation of the Transition Probabilities in 3-State Models. Journal of Statistical Software, 62(4), 1-29. doi: 10.18637/jss.v062.i04

Karl A. T., Eubank R., Milovanovic J., Reiser M., Young D. (2014). Using RngStreams for parallel random number generation in C++ and R. Computational Statistics, 29(5), 1301-1320. doi: 10.1007/s00180-014-0492-3

L'Ecuyer, P. (1999). Good parameters and implementations for combined multiple recursive random number generators. Operations Research, 47(1), 159–-164. doi: 10.1287/opre.47.1.159

L’Ecuyer P., Simard R., Chen E. J., Kelton W. D. (2002). An object-oriented random-number package with many long streams and substreams. Operations Research, 50(6), 1073–-1075. doi: 10.1287/opre.50.6.1073.358

Examples

# Set the number of threads
nth <- setThreadsTP(2)

# Define package seed
seed <- rep(x=1, times=6)

# Set package seed
setPackageSeedTP(seed)

# Create survTP object
data(heartTP)
heartTP_obj <- with(heartTP, survTP(time1, event1, Stime, event))

# Compute transition probabilities with confidence band
TPmsm0 <- transAJ(object=heartTP_obj, s=33, t=412, conf=TRUE,
conf.level=0.9, method.boot="percentile")

# Compute transition probabilities with confidence band
TPmsm1 <- transAJ(object=heartTP_obj, s=33, t=412, conf=TRUE,
conf.level=0.9, method.boot="percentile")

# The objects should be different
all.equal(TPmsm0, TPmsm1)

# Set package seed
setPackageSeedTP(seed)

# Compute transition probabilities with confidence band
TPmsm2 <- transAJ(object=heartTP_obj, s=33, t=412, conf=TRUE,
conf.level=0.9, method.boot="percentile")

# Both objects were computed from the same seed and should be equal
all.equal(TPmsm0, TPmsm2)

# Restore the number of threads
setThreadsTP(nth)

TPmsm documentation built on Jan. 14, 2023, 1:17 a.m.